Publications by authors named "Eve S Shalom"
Standard models for perfusion quantification in DCE-MRI produce a bias by treating voxels as isolated systems. Spatiotemporal models can remove this bias, but it is unknown whether they are fundamentally identifiable. The aim of this study is to investigate this question in silico using one-dimensional toy systems with a one-compartment blood flow model and a two-compartment perfusion model.
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- Purpose of the study was to standardize quantitative imaging methods for tumors, specifically using DCE-MRI, through the OSIPI-DCE challenge to benchmark these methods.
- Methods involved creating a framework for evaluating DCE-MRI analysis submissions from the perfusion MRI community, focusing on glioblastoma quantification and requiring detailed reporting of procedures and software.
- Results showed significant variability in software performance, with scores indicating differences in accuracy, repeatability, and reproducibility, while highlighting the importance of standardized procedures for improving analysis consistency.
In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions-of-interest as isolated systems supplied by a single global source. This simplification not only leads to long-recognized systematic errors but also fails to leverage the embedded spatial structure within the data.
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